AI Transparency and Explain-ability

Registration ID: IJNRD_208343

Published ID: IJNRD2311057

DOI: Click Here to Get

Sunidhi Chopada , Yukti

XAI, AI models, Artificial Intelligence

Abstract: Artificial intelligence (AI) is rapidly transforming the field of education, with the potential to provide personalized learning experiences, automate administrative tasks, and improve assessment outcomes. However, the implementation of AI in education also raises several ethical concerns, such as data privacy, bias, and the potential for AI to replace human teachers. Explainable AI (XAI) is a promising solution to the problem of transparency and interpretability in AI models. XAI provides a way to explain the decision-making process of AI models, making it easier for stakeholders to understand and trust the decisions made by AI models. This paper presents a systematic review of the opportunities and challenges of XAI for educational assessment. The review identifies the following key areas: Opportunities: XAI can be used to improve the transparency and fairness of automated grading systems, provide feedback to students and teachers on their performance, and develop new assessment methods that are more aligned with the needs of individual students. Challenges: Developing effective XAI methods for educational assessment is a challenging task, as educational assessment models are often complex and opaque. Additionally, it is important to consider the human factors involved in XAI, such as how to design XAI explanations that are understandable and useful for different stakeholders. The paper concludes with a discussion of future research directions in XAI for educational assessment.

Published Paper   E-Certificate

Sunidhi Chopada & Yukti (November-2023). AI Transparency and Explain-ability. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), a519-a521. https://ijnrd.org/papers/IJNRD2311057.pdf

Volume 8 Issue 11, November-2023

Pages : a519-a521

Paper Reg. ID: IJNRD_208343

Published Paper Id: IJNRD2311057

Downloads: 000121984

Research Area: Computer Science & Technology 

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2311057.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311057

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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